# rcontrib

##### Computes a measure of how correlated each variable in a set is with the other variable, conditional on a nominated subset of them

A measure of how correlated a variable is with those in a set is given by the
square root of the sum of squares of the correlation coefficients between the
variables and the other variables in the set (Cummings, 2007). Here, the partial
correlation between the subset of the variables listed in `response`

that
are not listed in `include`

is calculated from the partial correlation matrix
for the subset, adjusting for those variables in `include`

. This is useful
for manually deciding which of the variables not in `include`

should next be
added to it.

##### Usage

`rcontrib(responses, data, include = NULL)`

##### Arguments

- responses
A

`character`

giving the names of the columns in`data`

from which the correlation measure is to be calculated.- data
A

`data.frame`

containing the columns of variables from which the correlation measure is to be calculated.- include
A

`character`

giving the names of the columns in`data`

for the variables for which other variables are to be adjusted.

##### Value

A `numeric`

giving the correlation measures.

##### References

Cumming, J. A. and D. A. Wood (2007) Dimension reduction via principal variables. *Computational Statistics
and Data Analysis*, **52**, 550--565.

##### See Also

##### Examples

```
# NOT RUN {
data(exampleData)
responses <- c("Area","Area.SV","Area.TV", "Image.Biomass", "Max.Height","Centre.Mass",
"Density", "Compactness.TV", "Compactness.SV")
h <- rcontrib(responses, longi.dat, include = "Area")
# }
```

*Documentation reproduced from package growthPheno, version 1.0-22, License: GPL (>= 2)*